Active Directory Remediation via Graph Analysis
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Solution Overview
Problem
Managing and visualizing complex active directory structures is challenging due to difficulties in accurately editing, controlling, and remedying group hierarchies, access permissions, and identifying unused or low-confidence groups, which leads to security breaches and resource inefficiencies.
Innovation Solution
The solution involves an apparatus and method for active directory management and remediation that determines direct and indirect access based on group hierarchies, identifies owners, and performs automated remediation actions such as removing unnecessary access and unused groups, using data models, graph generators, and visualization tools to provide insights and clean up active directory structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual management of active directory group hierarchies is used, then flexibility in access control is maintained, but management complexity and time consumption increase significantly
Solution Approach 1:
The system performs automated remediation actions including self-identification of unused groups, self-detection of incorrect access grants, and self-execution of remediation tasks such as removing unnecessary access permissions and cleaning up group hierarchies without requiring manual intervention for each task
Solution Approach 2:
The patent replaces manual mechanical operations of managing active directory structures with an automated computational system that uses graph analysis algorithms to identify relationships, calculate confidence scores, and execute remediation actions automatically
2Reliability
If comprehensive access control monitoring is implemented, then security is improved, but system complexity and resource utilization increase
Solution Approach 1:
The system segments the complex active directory structure into discrete graph entities (users, groups, resources) with defined relationships, allowing individual analysis of each component and its access permissions without requiring analysis of the entire system at once
Solution Approach 2:
The patent introduces an intermediary graph analysis layer that sits between the active directory system and the monitoring functions, translating complex directory structures into analyzable graph representations with confidence scores that simplify security analysis
3Reliability
If automated remediation actions are performed, then security breaches are prevented, but risk of incorrect access removal increases
Solution Approach 1:
The system calculates confidence scores based on multiple factors including group hierarchy depth, membership counts, and access patterns, providing feedback that indicates the likelihood of correct versus incorrect access grants, allowing prioritized remediation of high-confidence issues
Solution Approach 2:
The patent performs preliminary analysis and identification of potential security issues before executing remediation actions, generating a ranked list of remediation targets based on confidence scores that allows review and approval before automatic correction
4Loss of information
If detailed visualization of group hierarchies is provided, then management insights are improved, but data processing requirements and resource usage increase
Solution Approach 1:
The system provides detailed visualization and analysis focused on specific local areas of the active directory structure that require attention, such as groups with low confidence scores or unusual access patterns, rather than uniformly processing and displaying information about all groups and users
Data Source
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AI summary
In some examples, active directory management and remediation may include obtaining data associated with active directories, and generating, based on the obtained data, a data model that includes a plurality of active directory concepts. A graph may be generated and include the active directory concepts as nodes and relationships between the nodes. The graph may be analyzed to identify user to group memberships. A membership score and a reasoning for the membership score may be determined for each identified user to group membership of the user to group memberships. The membership score and the reasoning for the membership score may be analyzed for each identified user to group membership. Further, based on the analysis of the membership score and the reasoning for the membership score, the at least one of the user to group memberships may be remediated.